Availability and Failure Rate of VNF Instances: Impacting Parameters and Calculation Methods
Bibliographic record
Abstract
Network Function Virtualization (NFV) defines a dynamic environment to deploy Virtual Network Functions (VNF) as constituents of Network Services (NS) that provide specific network functionalities. A VNF is composed of at least one VNF Component (VNFC) and zero, or more Internal Virtual Links (IntVL). The availability of an NS depends on the availability of the composing VNF functionalities. In turn these depend on the underlying resources, their placement constraints, policies, and their number, which change over time as required by the varying workload. Accordingly, the availability and failure rate of a VNF instance may vary over time. That is, it may be different for the different VNF scaling levels. In this paper, we investigate the parameters affecting the availability and the failure rate of a VNF instance, and we propose methods to calculate for such dynamic cases the guaranteed minimum availability and the guaranteed maximum failure rate for a VNF instance considering a given infrastructure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".